{"id":"W7131131954","doi":"10.1109/iccvw69036.2025.00276","title":"CSG-Fusion: Consistent Sparse-View Gaussian Splatting via Matching-based Fusion","year":2025,"lang":"","type":"article","venue":"","topic":"Random lasers and scattering media","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Merge (version control); Rendering (computer graphics); Gaussian; Feature matching; Matching (statistics); Feature (linguistics); Consistency (knowledge bases)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006708971,0.0007251481,0.001057501,0.0002393529,0.0008643263,0.000396023,0.0004557715,0.0002031031,0.007315729],"category_scores_gemma":[0.00001343928,0.0006318433,0.0006848095,0.0005390258,0.0001872175,0.0001608696,0.0003120686,0.0006154067,0.0005935513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001031795,"about_ca_system_score_gemma":0.0004601253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005741754,"about_ca_topic_score_gemma":0.00007083113,"domain_scores_codex":[0.9962783,0.0002439106,0.001193047,0.0009643977,0.000384045,0.0009362286],"domain_scores_gemma":[0.9978285,0.0003744286,0.0003730871,0.0009119018,0.000128258,0.0003838259],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001558663,0.003534832,0.07139207,0.003403793,0.001702057,0.0002463727,0.002159147,0.00708777,0.02121564,0.04955186,0.03220437,0.8059434],"study_design_scores_gemma":[0.06128262,0.0012693,0.03578589,0.02786429,0.004243216,0.00003260902,0.007115111,0.270896,0.04896373,0.04059147,0.4930485,0.008907255],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5602228,0.001999548,0.2773873,0.01510707,0.007284845,0.002274938,0.00008348522,0.0002258783,0.1354142],"genre_scores_gemma":[0.9860222,0.00005737774,0.001920416,0.001997967,0.0005610979,0.00005686514,0.0001075157,0.00005150313,0.009225067],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7970362,"threshold_uncertainty_score":0.9996133,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01069057355020727,"score_gpt":0.2428429607908464,"score_spread":0.2321523872406391,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}